Toggle navigation

.ipynb .pdf

repository

Contents

import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
from folium import plugins
import folium


pd.set_option('display.float_format', lambda x: '%0.4f' % x)

/Users/jnapolitano/venvs/sphinx-build/lib/python3.9/site-packages/geopandas/_compat.py:111: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.
  warnings.warn(

US Power Plants Meta Analysis

US Power Plants Data

gisfilepath = "/Users/jnapolitano/Projects/gas.jnapolitano.io/source/data/Power_Plants.geojson"

powerplants_df = gpd.read_file(gisfilepath)
#Selecting only Operational Plants
powerplants_df=powerplants_df.loc[powerplants_df['STATUS'] == 'OP'].copy()

#powerplants_df = powerplants_df.to_crs(epsg=3857)

powerplants_df

	OBJECTID	PLANT_CODE	NAME	ADDRESS	CITY	STATE	ZIP	TELEPHONE	TYPE	STATUS	...	NGAS_USED	OIL_USED	NET_GEN	CAP_FACTOR	SUB_1	SUB_2	LINES	SOURCE_LAT	SOURC_LONG	geometry
0	1	10504	AMALGAMATED SUGAR TWIN FALLS	2320 ORCHARD DRIVE EAST	TWIN FALLS	ID	83301	NOT AVAILABLE	CONVENTIONAL STEAM COAL	OP	...	1281711	0	51971.0000	0.6980	NOT AVAILABLE	NOT AVAILABLE	0	42.5325	-114.4320	POINT (-114.43202 42.53255)
2	3	7458	RUEDI	14151 FRYING PAN ROAD	ASPEN	CO	81611	NOT AVAILABLE	CONVENTIONAL HYDROELECTRIC	OP	...	0	0	22690.0000	0.5756	NOT AVAILABLE	NOT AVAILABLE	0	39.3631	-106.8219	POINT (-106.82194 39.36311)
3	4	6204	LARAMIE RIVER STATION	HWY 320	WHEATLAND	WY	82201	NOT AVAILABLE	CONVENTIONAL STEAM COAL	OP	...	0	37956	10282950.0000	0.6865	LARAMIE RIVER	NOT AVAILABLE	3	42.1091	-104.8839	POINT (-104.88388 42.10906)
4	5	8028	ARVADA	HWY 14	ARVADA	WY	82831	NOT AVAILABLE	NATURAL GAS FIRED COMBUSTION TURBINE	OP	...	2492	0	165.0000	0.0013	UNKNOWN204352	NOT AVAILABLE	1	44.6938	-105.9777	POINT (-105.97772 44.69378)
5	6	8026	HARTZOG	HWY 50	WRIGHT	WY	82732	NOT AVAILABLE	NATURAL GAS FIRED COMBUSTION TURBINE	OP	...	6018	0	370.0000	0.0028	UNKNOWN201474	NOT AVAILABLE	1	43.7734	-105.7808	POINT (-105.78081 43.77335)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
11805	11806	60487	RIVER ROAD SOLAR, LLC	951 RIVER ROAD	COFIELD	NC	27922	NOT AVAILABLE	SOLAR PHOTOVOLTAIC	OP	...	0	0	7833.0000	0.1788	NOT AVAILABLE	NOT AVAILABLE	0	36.3510	-76.8420	POINT (-76.86189 36.36233)
11806	11807	2720	BUCK	1555 DUKEVILLE RD	SALISBURY	NC	28145	(704) 382-4327	CONVENTIONAL STEAM COAL; NATURAL GAS FIRED COM...	OP	...	35735577	0	5173061.0000	0.8840	UNKNOWN114701	TAP169920	1	35.7133	-80.3767	POINT (-80.37562 35.70790)
11807	11808	56587	MINNESOTA BREEZE LLC	28431 KING AVENUE	RUSHMORE	MN	56168	NOT AVAILABLE	ONSHORE WIND TURBINE	OP	...	0	0	2892.0000	0.2540	NOT AVAILABLE	NOT AVAILABLE	0	43.5833	-95.7781	POINT (-95.77810 43.58330)
11808	11809	62139	HUNEKE II CSG	45000 145TH AVENUE	ZUMBROTA	MN	55992	NOT AVAILABLE	SOLAR PHOTOVOLTAIC	OP	...	0	0	288.0000	0.0299	NOT AVAILABLE	NOT AVAILABLE	0	44.2972	-92.7415	POINT (-92.73593 44.29588)
11809	11810	60834	HWY 14 HOLDCO SOLAR CSG	7647 COUNTRY CLUB ROAD W BYRON	BYRON	MN	55920	NOT AVAILABLE	SOLAR PHOTOVOLTAIC	OP	...	0	0	7943.0000	0.1813	NOT AVAILABLE	NOT AVAILABLE	0	44.0218	-92.5922	POINT (-92.59275 44.02356)

8736 rows × 45 columns

US Power Plants Interactive Map

operational_powerplants_map = powerplants_df.explore(column="PRIM_FUEL", # make choropleth based on "BoroName" column
     popup=False, # show all values in popup (on click)
     tiles="CartoDB positron", # use "CartoDB positron" tiles
     cmap="Set1", # use "Set1" matplotlib colormap
     style_kwds=dict(color="black"),
     marker_kwds= dict(radius=6),# use black outline)
     scheme = 'EqualInterval',
     k = 5
)
#ng_map
operational_powerplants_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Natural Gas Plants

Identifying the Natural Gas Plants in the Dataset

ng_plants = powerplants_df.loc[powerplants_df['PRIM_FUEL'] == 'NG'].copy()
ng_plants

	OBJECTID	PLANT_CODE	NAME	ADDRESS	CITY	STATE	ZIP	TELEPHONE	TYPE	STATUS	...	NGAS_USED	OIL_USED	NET_GEN	CAP_FACTOR	SUB_1	SUB_2	LINES	SOURCE_LAT	SOURC_LONG	geometry
4	5	8028	ARVADA	HWY 14	ARVADA	WY	82831	NOT AVAILABLE	NATURAL GAS FIRED COMBUSTION TURBINE	OP	...	2492	0	165.0000	0.0013	UNKNOWN204352	NOT AVAILABLE	1	44.6938	-105.9777	POINT (-105.97772 44.69378)
5	6	8026	HARTZOG	HWY 50	WRIGHT	WY	82732	NOT AVAILABLE	NATURAL GAS FIRED COMBUSTION TURBINE	OP	...	6018	0	370.0000	0.0028	UNKNOWN201474	NOT AVAILABLE	1	43.7734	-105.7808	POINT (-105.78081 43.77335)
6	7	8030	BARBER CREEK	INTERSTATE 90	GILLETTE	WY	82718	NOT AVAILABLE	NATURAL GAS FIRED COMBUSTION TURBINE	OP	...	2024	0	124.0000	0.0009	UNKNOWN201475	NOT AVAILABLE	1	44.1683	-105.8914	POINT (-105.89142 44.16833)
7	8	56606	CULBERTSON GENERATION STATION	HWY 16	CULBERTSON	MT	59218	NOT AVAILABLE	NATURAL GAS FIRED COMBUSTION TURBINE	OP	...	1033610	0	95987.0000	0.1207	UNKNOWN204459	NOT AVAILABLE	1	48.2102	-104.3918	POINT (-104.39180 48.21018)
8	9	55866	BASIN CREEK PLANT	500 NILS DRIVE	BUTTE	MT	59701	NOT AVAILABLE	NATURAL GAS INTERNAL COMBUSTION ENGINE	OP	...	845257	0	92242.0000	0.1950	UNKNOWN204463	NOT AVAILABLE	1	45.9298	-112.5249	POINT (-112.52486 45.92976)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
11769	11770	961	SOUTH MAIN STREET	1015 CARON RD	ROCHELLE	IL	61068	NOT AVAILABLE	NATURAL GAS INTERNAL COMBUSTION ENGINE; NATURA...	OP	...	0	2492	1458.0000	0.0116	NOT AVAILABLE	NOT AVAILABLE	0	41.9092	-89.0504	POINT (-89.05065 41.90949)
11794	11795	1258	ANTHONY	71D SW 20TH RD	ANTHONY	KS	67003	NOT AVAILABLE	NATURAL GAS INTERNAL COMBUSTION ENGINE	OP	...	2283	63	165.0000	0.0017	NOT AVAILABLE	NOT AVAILABLE	0	37.1517	-98.0311	POINT (-98.03110 37.15153)
11800	11801	59726	CML&P GENERATING FACILITY NO. 2	2601 N. 5TH INDUSTRIAL STREET	COFFEYVILLE	KS	67337	NOT AVAILABLE	NATURAL GAS INTERNAL COMBUSTION ENGINE	OP	...	502802	0	57987.0000	0.1180	NOT AVAILABLE	NOT AVAILABLE	0	37.0930	-95.5852	POINT (-95.58515 37.09298)
11803	11804	57884	LAKE CHARLES POLYMERS	4101 HIGHWAY 108 S	WESTLAKE	LA	70669	NOT AVAILABLE	NATURAL GAS FIRED COMBUSTION TURBINE; NATURAL ...	OP	...	1520931	0	141588.0000	0.5671	UNKNOWN116328	NOT AVAILABLE	1	30.1966	-93.3220	POINT (-93.32196 30.19664)
11806	11807	2720	BUCK	1555 DUKEVILLE RD	SALISBURY	NC	28145	(704) 382-4327	CONVENTIONAL STEAM COAL; NATURAL GAS FIRED COM...	OP	...	35735577	0	5173061.0000	0.8840	UNKNOWN114701	TAP169920	1	35.7133	-80.3767	POINT (-80.37562 35.70790)

1636 rows × 45 columns

Modifying Unit Scale for Map Legend

ng_plants['NGAS_USED']=ng_plants['NGAS_USED'].apply(lambda x: x/1)
ng_plants.rename(columns = {'NGAS_USED':'NGAS_BTUs'}, inplace = True)
ng_plants

	OBJECTID	PLANT_CODE	NAME	ADDRESS	CITY	STATE	ZIP	TELEPHONE	TYPE	STATUS	...	NGAS_BTUs	OIL_USED	NET_GEN	CAP_FACTOR	SUB_1	SUB_2	LINES	SOURCE_LAT	SOURC_LONG	geometry
4	5	8028	ARVADA	HWY 14	ARVADA	WY	82831	NOT AVAILABLE	NATURAL GAS FIRED COMBUSTION TURBINE	OP	...	2492.0000	0	165.0000	0.0013	UNKNOWN204352	NOT AVAILABLE	1	44.6938	-105.9777	POINT (-105.97772 44.69378)
5	6	8026	HARTZOG	HWY 50	WRIGHT	WY	82732	NOT AVAILABLE	NATURAL GAS FIRED COMBUSTION TURBINE	OP	...	6018.0000	0	370.0000	0.0028	UNKNOWN201474	NOT AVAILABLE	1	43.7734	-105.7808	POINT (-105.78081 43.77335)
6	7	8030	BARBER CREEK	INTERSTATE 90	GILLETTE	WY	82718	NOT AVAILABLE	NATURAL GAS FIRED COMBUSTION TURBINE	OP	...	2024.0000	0	124.0000	0.0009	UNKNOWN201475	NOT AVAILABLE	1	44.1683	-105.8914	POINT (-105.89142 44.16833)
7	8	56606	CULBERTSON GENERATION STATION	HWY 16	CULBERTSON	MT	59218	NOT AVAILABLE	NATURAL GAS FIRED COMBUSTION TURBINE	OP	...	1033610.0000	0	95987.0000	0.1207	UNKNOWN204459	NOT AVAILABLE	1	48.2102	-104.3918	POINT (-104.39180 48.21018)
8	9	55866	BASIN CREEK PLANT	500 NILS DRIVE	BUTTE	MT	59701	NOT AVAILABLE	NATURAL GAS INTERNAL COMBUSTION ENGINE	OP	...	845257.0000	0	92242.0000	0.1950	UNKNOWN204463	NOT AVAILABLE	1	45.9298	-112.5249	POINT (-112.52486 45.92976)
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
11769	11770	961	SOUTH MAIN STREET	1015 CARON RD	ROCHELLE	IL	61068	NOT AVAILABLE	NATURAL GAS INTERNAL COMBUSTION ENGINE; NATURA...	OP	...	0.0000	2492	1458.0000	0.0116	NOT AVAILABLE	NOT AVAILABLE	0	41.9092	-89.0504	POINT (-89.05065 41.90949)
11794	11795	1258	ANTHONY	71D SW 20TH RD	ANTHONY	KS	67003	NOT AVAILABLE	NATURAL GAS INTERNAL COMBUSTION ENGINE	OP	...	2283.0000	63	165.0000	0.0017	NOT AVAILABLE	NOT AVAILABLE	0	37.1517	-98.0311	POINT (-98.03110 37.15153)
11800	11801	59726	CML&P GENERATING FACILITY NO. 2	2601 N. 5TH INDUSTRIAL STREET	COFFEYVILLE	KS	67337	NOT AVAILABLE	NATURAL GAS INTERNAL COMBUSTION ENGINE	OP	...	502802.0000	0	57987.0000	0.1180	NOT AVAILABLE	NOT AVAILABLE	0	37.0930	-95.5852	POINT (-95.58515 37.09298)
11803	11804	57884	LAKE CHARLES POLYMERS	4101 HIGHWAY 108 S	WESTLAKE	LA	70669	NOT AVAILABLE	NATURAL GAS FIRED COMBUSTION TURBINE; NATURAL ...	OP	...	1520931.0000	0	141588.0000	0.5671	UNKNOWN116328	NOT AVAILABLE	1	30.1966	-93.3220	POINT (-93.32196 30.19664)
11806	11807	2720	BUCK	1555 DUKEVILLE RD	SALISBURY	NC	28145	(704) 382-4327	CONVENTIONAL STEAM COAL; NATURAL GAS FIRED COM...	OP	...	35735577.0000	0	5173061.0000	0.8840	UNKNOWN114701	TAP169920	1	35.7133	-80.3767	POINT (-80.37562 35.70790)

1636 rows × 45 columns

Natural Gas Plants Interactive Map

ng_map = ng_plants.explore(column="NGAS_BTUs", # make choropleth based on "BoroName" column
     popup=True, # show all values in popup (on click)
     tiles="CartoDB positron", # use "CartoDB positron" tiles
     cmap="Set1", # use "Set1" matplotlib colormap
     style_kwds=dict(color="black"),
     marker_kwds= dict(radius=6),# use black outline)
     scheme = 'EqualInterval',
     k = 5

)
ng_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Natural Gas Plant Density Interactive Map

heat_data = [[point.xy[1][0], point.xy[0][0]] for point in ng_plants.geometry ]

mapTest = folium.Map(location = [30, -90], tiles='Cartodb dark_matter', zoom_start = 4)

test = plugins.HeatMap(data = heat_data, show=True)
test.add_to(mapTest)

mapTest

Make this Notebook Trusted to load map: File -> Trust Notebook

Coal Plants

Identifying the Coal Plants in the Dataset

#powerplants_df.PRIM_FUEL.unique()
#RC,WC,NUC
#Selecting only Operational Plants
#powerplants_df=powerplants_df.loc[powerplants_df['STATUS'] == 'OP'].copy()

#selecting coal plants
wc_plants = powerplants_df.loc[powerplants_df['PRIM_FUEL'] == 'WC'].copy()
rc_plants = powerplants_df.loc[powerplants_df['PRIM_FUEL'] == 'RC'].copy()

#concatenating coal type dfs into one
coal_df = pd.concat([wc_plants,rc_plants])
coal_df.columns

Index(['OBJECTID', 'PLANT_CODE', 'NAME', 'ADDRESS', 'CITY', 'STATE', 'ZIP',
       'TELEPHONE', 'TYPE', 'STATUS', 'COUNTY', 'COUNTYFIPS', 'COUNTRY',
       'LATITUDE', 'LONGITUDE', 'NAICS_CODE', 'NAICS_DESC', 'SOURCE',
       'SOURCEDATE', 'VAL_METHOD', 'VAL_DATE', 'WEBSITE', 'OPERATOR',
       'OPERAT_ID', 'OPER_CAP', 'SUMMER_CAP', 'WINTER_CAP', 'PLAN_CAP',
       'RETIRE_CAP', 'GEN_UNITS', 'PLAN_UNIT', 'RETIR_UNIT', 'PRIM_FUEL',
       'SEC_FUEL', 'COAL_USED', 'NGAS_USED', 'OIL_USED', 'NET_GEN',
       'CAP_FACTOR', 'SUB_1', 'SUB_2', 'LINES', 'SOURCE_LAT', 'SOURC_LONG',
       'geometry'],
      dtype='object')

Coal Plants Interactive Map

coal_map = coal_df.explore(column="NET_GEN", # make choropleth based on "BoroName" column
     popup=True, # show all values in popup (on click)
     tiles="CartoDB positron", # use "CartoDB positron" tiles
     cmap="Set1", # use "Set1" matplotlib colormap
     style_kwds=dict(color="black"),
     marker_kwds= dict(radius=6),# use black outline)
     scheme = 'EqualInterval',
     k = 5

)
coal_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Coal Plant Density Interactive Map

coal_heat_data = [[point.xy[1][0], point.xy[0][0]] for point in coal_df.geometry ]
#

coal_density_map = folium.Map(location = [30, -90], tiles='Cartodb dark_matter', zoom_start = 4)

coal_heat_layer = plugins.HeatMap(data = coal_heat_data, show=True)
coal_heat_layer.add_to(coal_density_map)

coal_density_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Solar Plants

Identifying the Solar Plants in the Dataset

#powerplants_df.PRIM_FUEL.unique()
#RC,WC,NUC
#Selecting only Operational Plants
#powerplants_df=powerplants_df.loc[powerplants_df['STATUS'] == 'OP'].copy()

#selecting coal plants
solar_plants = powerplants_df.loc[powerplants_df['PRIM_FUEL'] == 'SUN'].copy()

#concatenating coal type dfs into one
solar_plants.columns

Index(['OBJECTID', 'PLANT_CODE', 'NAME', 'ADDRESS', 'CITY', 'STATE', 'ZIP',
       'TELEPHONE', 'TYPE', 'STATUS', 'COUNTY', 'COUNTYFIPS', 'COUNTRY',
       'LATITUDE', 'LONGITUDE', 'NAICS_CODE', 'NAICS_DESC', 'SOURCE',
       'SOURCEDATE', 'VAL_METHOD', 'VAL_DATE', 'WEBSITE', 'OPERATOR',
       'OPERAT_ID', 'OPER_CAP', 'SUMMER_CAP', 'WINTER_CAP', 'PLAN_CAP',
       'RETIRE_CAP', 'GEN_UNITS', 'PLAN_UNIT', 'RETIR_UNIT', 'PRIM_FUEL',
       'SEC_FUEL', 'COAL_USED', 'NGAS_USED', 'OIL_USED', 'NET_GEN',
       'CAP_FACTOR', 'SUB_1', 'SUB_2', 'LINES', 'SOURCE_LAT', 'SOURC_LONG',
       'geometry'],
      dtype='object')

Solar Plants Interactive Map

solar_map = solar_plants.explore(column="NET_GEN", # make choropleth based on "BoroName" column
     popup=True, # show all values in popup (on click)
     tiles="CartoDB positron", # use "CartoDB positron" tiles
     cmap="Set1", # use "Set1" matplotlib colormap
     style_kwds=dict(color="black"),
     marker_kwds= dict(radius=6),# use black outline)
     scheme = 'EqualInterval',
     k = 5

)
solar_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Solar Plant Density Interactive Map

solar_heat_data = [[point.xy[1][0], point.xy[0][0]] for point in solar_plants.geometry ]
#

solar_density_map = folium.Map(location = [30, -90], tiles='Cartodb dark_matter', zoom_start = 4)

solar_heat_layer = plugins.HeatMap(data = solar_heat_data, show=True)
solar_heat_layer.add_to(solar_density_map)

solar_density_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Wind Plants

Identifying the Coal Plants in the Dataset

#powerplants_df.PRIM_FUEL.unique()
#RC,WC,NUC
#Selecting only Operational Plants
#powerplants_df=powerplants_df.loc[powerplants_df['STATUS'] == 'OP'].copy()

#selecting coal plants
wind_plants = powerplants_df.loc[powerplants_df['PRIM_FUEL'] == 'WND'].copy()

#concatenating coal type dfs into one
wind_plants.columns

Index(['OBJECTID', 'PLANT_CODE', 'NAME', 'ADDRESS', 'CITY', 'STATE', 'ZIP',
       'TELEPHONE', 'TYPE', 'STATUS', 'COUNTY', 'COUNTYFIPS', 'COUNTRY',
       'LATITUDE', 'LONGITUDE', 'NAICS_CODE', 'NAICS_DESC', 'SOURCE',
       'SOURCEDATE', 'VAL_METHOD', 'VAL_DATE', 'WEBSITE', 'OPERATOR',
       'OPERAT_ID', 'OPER_CAP', 'SUMMER_CAP', 'WINTER_CAP', 'PLAN_CAP',
       'RETIRE_CAP', 'GEN_UNITS', 'PLAN_UNIT', 'RETIR_UNIT', 'PRIM_FUEL',
       'SEC_FUEL', 'COAL_USED', 'NGAS_USED', 'OIL_USED', 'NET_GEN',
       'CAP_FACTOR', 'SUB_1', 'SUB_2', 'LINES', 'SOURCE_LAT', 'SOURC_LONG',
       'geometry'],
      dtype='object')

Wind Plants Interactive Map

wind_map = wind_plants.explore(column="NET_GEN", # make choropleth based on "BoroName" column
     popup=True, # show all values in popup (on click)
     tiles="CartoDB positron", # use "CartoDB positron" tiles
     cmap="Set1", # use "Set1" matplotlib colormap
     style_kwds=dict(color="black"),
     marker_kwds= dict(radius=6),# use black outline)
     scheme = 'EqualInterval',
     k = 5

)
wind_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Wind Plant Density Interactive Map

wind_heat_data = [[point.xy[1][0], point.xy[0][0]] for point in wind_plants.geometry ]
#

wind_density_map = folium.Map(location = [30, -90], tiles='Cartodb dark_matter', zoom_start = 4)

wind_heat_layer = plugins.HeatMap(data = wind_heat_data)
wind_heat_layer.add_to(wind_density_map)

wind_density_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Hydro Plants

Identifying the Hydro Plants in the Dataset

#powerplants_df.PRIM_FUEL.unique()
#RC,WC,NUC
#Selecting only Operational Plants
#powerplants_df=powerplants_df.loc[powerplants_df['STATUS'] == 'OP'].copy()

#selecting coal plants
hydro_plants = powerplants_df.loc[powerplants_df['PRIM_FUEL'] == 'WAT'].copy()

#concatenating coal type dfs into one
hydro_plants.columns

Index(['OBJECTID', 'PLANT_CODE', 'NAME', 'ADDRESS', 'CITY', 'STATE', 'ZIP',
       'TELEPHONE', 'TYPE', 'STATUS', 'COUNTY', 'COUNTYFIPS', 'COUNTRY',
       'LATITUDE', 'LONGITUDE', 'NAICS_CODE', 'NAICS_DESC', 'SOURCE',
       'SOURCEDATE', 'VAL_METHOD', 'VAL_DATE', 'WEBSITE', 'OPERATOR',
       'OPERAT_ID', 'OPER_CAP', 'SUMMER_CAP', 'WINTER_CAP', 'PLAN_CAP',
       'RETIRE_CAP', 'GEN_UNITS', 'PLAN_UNIT', 'RETIR_UNIT', 'PRIM_FUEL',
       'SEC_FUEL', 'COAL_USED', 'NGAS_USED', 'OIL_USED', 'NET_GEN',
       'CAP_FACTOR', 'SUB_1', 'SUB_2', 'LINES', 'SOURCE_LAT', 'SOURC_LONG',
       'geometry'],
      dtype='object')

Hydro Plants Interactive Map

hydro_map = hydro_plants.explore(column="NET_GEN", # make choropleth based on "BoroName" column
     popup=True, # show all values in popup (on click)
     tiles="CartoDB positron", # use "CartoDB positron" tiles
     cmap="Set1", # use "Set1" matplotlib colormap
     style_kwds=dict(color="black"),
     marker_kwds= dict(radius=6),# use black outline)
     scheme = 'Percentiles',
     k = 5

)
hydro_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Hydro Plant Density Interactive Map

hydro_heat_data = [[point.xy[1][0], point.xy[0][0]] for point in hydro_plants.geometry ]
#

hydro_density_map = folium.Map(location = [30, -90], tiles='Cartodb dark_matter', zoom_start = 4)

hydro_heat_layer = plugins.HeatMap(data = hydro_heat_data, show=True)
hydro_heat_layer.add_to(hydro_density_map)

hydro_density_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Nuclear Plants

Identifying the Nuclear Plants in the Dataset

#powerplants_df.PRIM_FUEL.unique()
#RC,WC,NUC
#Selecting only Operational Plants
#powerplants_df=powerplants_df.loc[powerplants_df['STATUS'] == 'OP'].copy()

#selecting coal plants
nuclear_plants = powerplants_df.loc[powerplants_df['PRIM_FUEL'] == 'NUC'].copy()

#concatenating coal type dfs into one
nuclear_plants.columns

Index(['OBJECTID', 'PLANT_CODE', 'NAME', 'ADDRESS', 'CITY', 'STATE', 'ZIP',
       'TELEPHONE', 'TYPE', 'STATUS', 'COUNTY', 'COUNTYFIPS', 'COUNTRY',
       'LATITUDE', 'LONGITUDE', 'NAICS_CODE', 'NAICS_DESC', 'SOURCE',
       'SOURCEDATE', 'VAL_METHOD', 'VAL_DATE', 'WEBSITE', 'OPERATOR',
       'OPERAT_ID', 'OPER_CAP', 'SUMMER_CAP', 'WINTER_CAP', 'PLAN_CAP',
       'RETIRE_CAP', 'GEN_UNITS', 'PLAN_UNIT', 'RETIR_UNIT', 'PRIM_FUEL',
       'SEC_FUEL', 'COAL_USED', 'NGAS_USED', 'OIL_USED', 'NET_GEN',
       'CAP_FACTOR', 'SUB_1', 'SUB_2', 'LINES', 'SOURCE_LAT', 'SOURC_LONG',
       'geometry'],
      dtype='object')

Nuclear Plants Interactive Map

nuclear_map = nuclear_plants.explore(column="NET_GEN", # make choropleth based on "BoroName" column
     popup=True, # show all values in popup (on click)
     tiles="CartoDB positron", # use "CartoDB positron" tiles
     cmap="Set1", # use "Set1" matplotlib colormap
     style_kwds=dict(color="black"),
     marker_kwds= dict(radius=6),# use black outline)
     scheme = 'EqualInterval',
     k = 5

)
nuclear_map

Make this Notebook Trusted to load map: File -> Trust Notebook

Nuclear Plant Density Interactive Map

nuclear_heat_data = [[point.xy[1][0], point.xy[0][0]] for point in solar_plants.geometry ]
#

nuclear_density_map = folium.Map(location = [30, -90], tiles='Cartodb dark_matter', zoom_start = 4)

nuclear_heat_layer = plugins.HeatMap(data = nuclear_heat_data, show=True)
nuclear_heat_layer.add_to(nuclear_density_map)

nuclear_density_map

Make this Notebook Trusted to load map: File -> Trust Notebook

previous

US Natural Gas Analysis

next

Contact Justin Napolitano